Vidas Daudaravicius

Also published as: Vidas Daudaravičius


2019

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Textual and Visual Characteristics of Mathematical Expressions in Scholar Documents
Vidas Daudaravicius
Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications

Mathematical expressions (ME) are widely used in scholar documents. In this paper we analyze characteristics of textual and visual MEs characteristics for the image-to-LaTeX translation task. While there are open data-sets of LaTeX files with MEs included it is very complicated to extract these MEs from a document and to compile the list of MEs. Therefore we release a corpus of open-access scholar documents with PDF and JATS-XML parallel files. The MEs in these documents are LaTeX encoded and are document independent. The data contains more than 1.2 million distinct annotated formulae and more than 80 million raw tokens of LaTeX MEs in more than 8 thousand documents. While the variety of textual lengths and visual sizes of MEs are not well defined we found that the task of analyzing MEs in scholar documents can be reduced to the subtask of a particular text length, image width and height bounds, and display MEs can be processed as arrays of partial MEs.

2016

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A Report on the Automatic Evaluation of Scientific Writing Shared Task
Vidas Daudaravicius | Rafael E. Banchs | Elena Volodina | Courtney Napoles
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications

2015

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Automated Evaluation of Scientific Writing: AESW Shared Task Proposal
Vidas Daudaravičius
Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications

2014

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Language Editing Dataset of Academic Texts
Vidas Daudaravičius
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We describe the VTeX Language Editing Dataset of Academic Texts (LEDAT), a dataset of text extracts from scientific papers that were edited by professional native English language editors at VTeX. The goal of the LEDAT is to provide a large data resource for the development of language evaluation and grammar error correction systems for the scientific community. We describe the data collection and the compilation process of the LEDAT. The new dataset can be used in many NLP studies and applications where deeper knowledge of the academic language and language editing is required. The dataset can be used also as a knowledge base of English academic language to support many writers of scientific papers.

2013

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VTEX System Description for the NLI 2013 Shared Task
Vidas Daudaravičius
Proceedings of the Eighth Workshop on Innovative Use of NLP for Building Educational Applications

2012

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VTEX Determiner and Preposition Correction System for the HOO 2012 Shared Task
Vidas Daudaravičius
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

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Applying Collocation Segmentation to the ACL Anthology Reference Corpus
Vidas Daudaravičius
Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries

2010

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Integration of statistical collocation segmentations in a phrase-based statistical machine translation system
Marta R. Costa-jussa | Vidas Daudaravicius | Rafael E. Banchs
Proceedings of the 14th Annual conference of the European Association for Machine Translation

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Characteristics and Analysis of Finnish and Swedish Clinical Intensive Care Nursing Narratives
Helen Allvin | Elin Carlsson | Hercules Dalianis | Riitta Danielsson-Ojala | Vidas Daudaravicius | Martin Hassel | Dimitrios Kokkinakis | Heljä Lundgren-Laine | Gunnar Nilsson | Øystein Nytrø | Sanna Salanterä | Maria Skeppstedt | Hanna Suominen | Sumithra Velupillai
Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents

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Using Collocation Segmentation to Augment the Phrase Table
Carlos A. Henríquez Q. | Marta Ruiz Costa-jussà | Vidas Daudaravicius | Rafael E. Banchs | José B. Mariño
Proceedings of the Joint Fifth Workshop on Statistical Machine Translation and MetricsMATR

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UPC-BMIC-VDU system description for the IWSLT 2010: testing several collocation segmentations in a phrase-based SMT system
Carlos Henríquez | Marta R. Costa-jussà | Vidas Daudaravicius | Rafael E. Banchs | José B. Mariño
Proceedings of the 7th International Workshop on Spoken Language Translation: Evaluation Campaign

This paper describes the UPC-BMIC-VMU participation in the IWSLT 2010 evaluation campaign. The SMT system is a standard phrase-based enriched with novel segmentations. These novel segmentations are computed using statistical measures such as Log-likelihood, T-score, Chi-squared, Dice, Mutual Information or Gravity-Counts. The analysis of translation results allows to divide measures into three groups. First, Log-likelihood, Chi-squared and T-score tend to combine high frequency words and collocation segments are very short. They improve the SMT system by adding new translation units. Second, Mutual Information and Dice tend to combine low frequency words and collocation segments are short. They improve the SMT system by smoothing the translation units. And third, GravityCounts tends to combine high and low frequency words and collocation segments are long. However, in this case, the SMT system is not improved. Thus, the road-map for translation system improvement is to introduce new phrases with either low frequency or high frequency words. It is hard to introduce new phrases with low and high frequency words in order to improve translation quality. Experimental results are reported in the French-to-English IWSLT 2010 evaluation where our system was ranked 3rd out of nine systems.

2007

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Morphological Annotation of the Lithuanian Corpus
Erika Rimkutė | Vidas Daudaravičius | Andrius Utka
Proceedings of the Workshop on Balto-Slavonic Natural Language Processing